EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-based embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

This 29-color panel was developed and optimized for the monitoring of NK cell and T cell reconstitution in peripheral blood of patients after HSCT. We considered major post-HSCT complications during the design, such as relapses, viral infections, and GvHD and identification of lymphocyte populations relevant to their resolution. The panel includes markers for all major NK cell and T cell subsets and analysis of their development and qualitative properties. In the NK cell compartment, we focus mainly on CD57 + NKG2C+ cells and the expression of activating (NKG2D, DNAM-1) and inhibitory receptors (NKG2A, TIGIT). Another priority is the characterization of T cell reconstitution; therefore, we included detection of CD4+ RTEs based on CD45RA, CD62L, CD95, and CD31 as a marker of thymus function. Besides that, we also analyze the emergence and properties of major T cell populations with a particular interest in CD8, Th1, ThCTL, and Treg subsets. Overall, the panel allows for comprehensive analysis of the reconstituting immune system and identification of potential markers of immune cell dysfunction.

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E cient unbiased data analysis is a major challenge for laboratories handling large cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analyses by providing high-performance visualization of complex single cell distributions within cellular populations and their transition states. e algorithm is designed for linear scaling and speed suitable for interactive analyses of millions of cells without downsampling. At the same time, the visualization quality is competitive with current stateof-art algorithms. We demonstrate the properties of EmbedSOM on work ows that improve two essential types of analyses: e native ability of EmbedSOM to align population positions in embedding is used for comparative analysis of multi-sample data, and the connection to FlowSOM is exploited for simplifying the supervised hierarchical dissection of cell populations. Additionally, we discuss the visualization of the trajectories between cellular states facilitated by the local linearity of the embedding.